Frequently asked questions

BackEngine is a customer context layer that continuously turns authorized company data into governed, permission-aware, source-linked context for AI systems. It sits between the tools where customer information lives and the AI your team already uses. Read the fuller explanation on What is BackEngine?

Customer knowledge is scattered across calls, email, CRM, support, chat, documents, and feedback systems. BackEngine cleans, deduplicates, joins, and organizes that authorized information before a question is asked, so AI receives a focused account history instead of isolated raw records.

BackEngine connects to authorized source systems, removes noise and duplicates, resolves people and accounts across tools, and builds a continuously updated customer graph and semantic index. At query time, it applies the asking user's permissions and returns relevant, source-linked context. See How BackEngine works.

A direct connector retrieves records from one source at query time. BackEngine prepares the cross-system context in advance: it cleans data, resolves identities, joins related records, and applies one permission model. In BackEngine's published head-to-head benchmark, that approach produced 64% fewer factual errors, surfaced 2.5× more critical facts, and used 81% fewer tokens. Review the comparison and the benchmark methodology.

Teams can use BackEngine from AI assistants that support the connection, including Claude and ChatGPT. BackEngine exposes governed customer context through its MCP server and supplies the instructions needed to use that context for real workflows. Learn more on the MCP page.

BackEngine reads authorized information about the customers and prospects you choose to track. It does not need unrelated employee or personal conversations. The connected systems, account scope, and access rules are established during setup.

BackEngine applies permissions by user, team, and account group across the joined customer data. A rep can be limited to their accounts, a manager can see their team's accounts, and a user can receive an authorized summary without gaining access to restricted source material. Read the security and privacy overview.

No. Customer data is used to provide the BackEngine service, not to train AI models. It remains owned and controlled by the customer and is encrypted in transit and at rest.

BackEngine is hosted on AWS in North America. Customer data is isolated by company and protected with per-company encryption keys. Detailed architecture and control evidence are available in the BackEngine Trust Center.

BackEngine maintains security documentation for customer review, including information about data flow, permissions, subprocessors, and controls. BackEngine is SOC 2 and HIPAA compliant and can sign BAAs. See the subprocessor list and the Trust Center.

Supported categories include CRM, call recordings, phone, email and calendar, chat, support, documents, project tools, and customer feedback. Current examples include Salesforce, HubSpot, Zoom, Gong, Fireflies, Gmail, Microsoft Outlook, Slack, Microsoft Teams, Zendesk, Intercom, Google Drive, Jira, and Enterpret. The current list is on Connect & Setup.

The customer's part is usually about 15 minutes on one authorization call. BackEngine handles configuration and historical backfill; data is typically cleaned and ready in about two days, with teams getting value within the first week.

No, on purpose. A BackEngine team member walks through authorization and setup so the right systems, account scope, permissions, and first workflows are configured correctly. Customers are not handed a blank account and left to design the rollout alone.

BackEngine is designed for messy operational data such as long email threads, transcripts, duplicate records, and inconsistent identifiers. It removes noise, resolves identities, and joins related information so AI can reason over the complete customer picture.

Common workflows include meeting preparation, account risk review, forecast validation, customer commitments, win-loss analysis, feature-request synthesis, expansion discovery, QBR preparation, and source-grounded customer summaries. See BackEngine in action.

Yes. Workflows can run on a schedule to produce outputs such as meeting briefs, account summaries, and forecast checks. The same governed context and permissions apply whether a user asks directly or a scheduled workflow runs automatically.

BackEngine supports work across sales, customer success, revenue operations, product, marketing, AI enablement, and executive leadership. Each role can use the same underlying customer evidence for the signals and workflows relevant to its decisions.

The economic value generally comes from lower AI processing cost, better decisions on renewals, expansion, pipeline, and product priorities, and less time spent gathering and reconciling customer information. The ROI overview explains those three categories.

Pricing is based on the size of the customer graph—the number of customers and prospects being tracked—not on the number of people using BackEngine. Seats and AI agents are included. See the pricing page for the current model.

It is possible to build internally, but the work extends beyond connectors. A production system also needs source-specific cleanup, identity resolution, cross-system joins, permissions, semantic retrieval, citations, monitoring, and ongoing maintenance. Review the build-versus-buy breakdown.

Start with the published benchmark, review the security model, and identify one customer-data workflow where your team can compare current effort and answer quality against BackEngine. To see it using your own environment, request a demo.